{"title":"Enhanced Elman Spike neural network optimized with Red Fox optimization algorithm for sugarcane yield grade prediction","authors":"M. Deepanayaki, Vidyaathulasiraman","doi":"10.1080/23080477.2023.2229173","DOIUrl":null,"url":null,"abstract":"ABSTRACT In this manuscript, Enhanced Elman Spike Neural Network (EESNN) optimized with Red Fox optimization algorithm is proposed for Sugarcane Yield Grade Prediction (SYGD-EESNN-RFOA). Initially, the sugar yield prediction dataset is taken. Then the input data are pre-processed by hybrid decomposition method that is morphological filtering and extended empirical wavelet transformation (MF-EEWT) to retrieve the missing values. These pre-processed outputs are given to feature selection methods. During the process of feature selection, Entropy – Kurtosis-based feature selection method (EKBFS) is applied. These extracted features are fed to EESNN, and then it classifies the sugarcane yield as low grade, medium grade, and high grade. Generally, EESNN method does not indicate the use of any optimization strategies for calculating the best parameters to ensure accurate sugarcane yield forecast. To forecast the sugarcane production accurately, the Red Fox Optimization Algorithm (RFOA) is proposed. The proposed approach is carried out in Python; its performance is evaluated under performance metrics, such as precision, root mean square error, mean square error, mean absolute percentage error, convergence curve, and predicted percentage of changes in sugarcane yield during 2021–2027. The proposed SYGP-EESNN-RFOA framework attains higher accuracy of 27.5%, 16.65%, and 9.13%, 15.21% higher specificity compared with the existing methods. Graphical abstract In this manuscript, Enhanced Elman Spike Neural Network (EESNN) optimized with Red Fox optimization algorithm is proposed for Sugarcane Yield Grade Prediction (SYGD-EESNN-RFOA). EESNN method does not indicate the use of any optimization strategies for calculating the best parameters to ensure accurate sugarcane yield forecast.","PeriodicalId":53436,"journal":{"name":"Smart Science","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Smart Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/23080477.2023.2229173","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
ABSTRACT In this manuscript, Enhanced Elman Spike Neural Network (EESNN) optimized with Red Fox optimization algorithm is proposed for Sugarcane Yield Grade Prediction (SYGD-EESNN-RFOA). Initially, the sugar yield prediction dataset is taken. Then the input data are pre-processed by hybrid decomposition method that is morphological filtering and extended empirical wavelet transformation (MF-EEWT) to retrieve the missing values. These pre-processed outputs are given to feature selection methods. During the process of feature selection, Entropy – Kurtosis-based feature selection method (EKBFS) is applied. These extracted features are fed to EESNN, and then it classifies the sugarcane yield as low grade, medium grade, and high grade. Generally, EESNN method does not indicate the use of any optimization strategies for calculating the best parameters to ensure accurate sugarcane yield forecast. To forecast the sugarcane production accurately, the Red Fox Optimization Algorithm (RFOA) is proposed. The proposed approach is carried out in Python; its performance is evaluated under performance metrics, such as precision, root mean square error, mean square error, mean absolute percentage error, convergence curve, and predicted percentage of changes in sugarcane yield during 2021–2027. The proposed SYGP-EESNN-RFOA framework attains higher accuracy of 27.5%, 16.65%, and 9.13%, 15.21% higher specificity compared with the existing methods. Graphical abstract In this manuscript, Enhanced Elman Spike Neural Network (EESNN) optimized with Red Fox optimization algorithm is proposed for Sugarcane Yield Grade Prediction (SYGD-EESNN-RFOA). EESNN method does not indicate the use of any optimization strategies for calculating the best parameters to ensure accurate sugarcane yield forecast.
期刊介绍:
Smart Science (ISSN 2308-0477) is an international, peer-reviewed journal that publishes significant original scientific researches, and reviews and analyses of current research and science policy. We welcome submissions of high quality papers from all fields of science and from any source. Articles of an interdisciplinary nature are particularly welcomed. Smart Science aims to be among the top multidisciplinary journals covering a broad spectrum of smart topics in the fields of materials science, chemistry, physics, engineering, medicine, and biology. Smart Science is currently focusing on the topics of Smart Manufacturing (CPS, IoT and AI) for Industry 4.0, Smart Energy and Smart Chemistry and Materials. Other specific research areas covered by the journal include, but are not limited to: 1. Smart Science in the Future 2. Smart Manufacturing: -Cyber-Physical System (CPS) -Internet of Things (IoT) and Internet of Brain (IoB) -Artificial Intelligence -Smart Computing -Smart Design/Machine -Smart Sensing -Smart Information and Networks 3. Smart Energy and Thermal/Fluidic Science 4. Smart Chemistry and Materials